s tablets for participants) providing computer or internet guidance to participants outside the group sessions, evaluating the eHealth attitude and skills of trainers, and tailoring eHealth training to increase the skills of future e-IMR trainers.
Netherlands Trial Register NTR4772; https//www.trialregister.nl/trial/4621.
RR2-10.1186/s12913-016-1267-z.
RR2-10.1186/s12913-016-1267-z.eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection.
The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center.
We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC).
Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found hcollect data related to cardiovascular disease and its risk factors.
We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps.
This study aimed to reveal the profiles of users of a mental health app through machine learning techniques.
We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login.
The results showed the following four different profiles of patients (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features.
User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.
User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.Acute otitis media (AOM) is the most common pediatric bacterial ear infection. AOM presents challenges to parents who lack accurate information. Digital knowledge translation tools offer a promising approach to communicating complex health information. We developed AOM knowledge translation tools for Canadian parents and augmented them for Pakistani parent end users.
This pilot study aimed to (1) develop AOM knowledge translation tools for Canadian parents, (2) adapt the knowledge translation tools across cultural contexts, and (3) evaluate the usability of the adapted knowledge translation tools.
Parents' perceptions of the translated knowledge translation tools' usability were explored using a mixed-methods design. We recruited parent participants from a hospital in Pakistan to complete usability surveys (n=47) and focus group interviews (n=21). Descriptive statistics and content analysis were used to analyze data.
Usability results showed the usefulness and effectiveness of both adapted knowledge tloring the impact of knowledge translation tools on child health outcomes.Due to the complexity and chronicity of heart failure, engaging yet simple patient self-management tools are needed.
This study aimed to assess the feasibility and patient engagement with a smartphone app designed for heart failure.
Patients with heart failure were randomized to intervention (smartphone with the Habits Heart App installed and Bluetooth-linked scale) or control (paper education material) groups. All intervention group patients were interviewed and monitored closely for app feasibility while receiving standard of care heart failure management by cardiologists. https://www.selleckchem.com/products/gsk3326595-epz015938.html The Atlanta Heart Failure Knowledge Test, a quality of life survey (Kansas City Cardiomyopathy Questionnaire), and weight were assessed at baseline and final visits.
Patients (N=28 patients; intervention n=15; control n=13) with heart failure (with reduced ejection fraction 15/28, 54%; male 20/28, 71%, female 8/28, 29%; median age 63 years) were enrolled, and 82% of patients (N=23; intervention 12/15, 80%; control 11/13, 85%) compnked scale is a feasible way to engage patients in heart failure management, and barriers to app engagement were identified. A larger multicenter study may be warranted to evaluate the effectiveness of the app.
ClinicalTrials.gov NCT03238729; http//clinicaltrials.gov/ct2/show/NCT03238729.
ClinicalTrials.gov NCT03238729; http//clinicaltrials.gov/ct2/show/NCT03238729.Cognitive impairment is one of the most debilitating manifestations of multiple sclerosis. Currently, the assessment of cognition relies on a time-consuming and extensive neuropsychological examination, which is only available in some centers.
To enable simpler, more accessible cognitive screening, we sought to determine the feasibility and potential assessment sensitivity of an unsupervised, adaptive, video game-based digital therapeutic to assess cognition in multiple sclerosis.
A total of 100 people with multiple sclerosis (33 with cognitive impairment and 67 without cognitive impairment) and 24 adults without multiple sclerosis were tested with the tablet game (EVO Monitor) and standard measures, including the Brief International Cognitive Assessment for Multiple Sclerosis (which included the Symbol Digit Modalities Test [SDMT]) and Multiple Sclerosis Functional Composite 4 (which included the Timed 25-Foot Walk test). Patients with multiple sclerosis also underwent neurological evaluations and contributed recent structural magnetic resonance imaging scans.